AI coding tools are moving beyond autocomplete.
For developers, indie hackers, SaaS builders, and website owners, the real question is no longer whether AI can write code. The better question is whether AI can reliably take part in the full software development workflow: understanding requirements, planning tasks, writing code, reviewing quality, testing results, updating docs, and fitting into a real team process.
Recently, ByteDance’s TRAE team published the 2026 Enterprise AI Coding Practice Handbook. One section stands out: Agent Skills Top 10.
It is one of the first public AI coding Skill recommendation lists from a major tech company.
What Is TRAE?
TRAE is an enterprise AI coding tool built by ByteDance based on the Doubao-Seed-2.0-Code model.
Its goal is not just better code completion. It aims to let AI participate more deeply in the software development process and move closer to the role of an “AI engineer.”
That means AI is expected to help with more than a single code snippet. It can take part in:
- Requirement breakdown
- Task planning
- Frontend and backend implementation
- Code review
- Testing
- Documentation maintenance
- Pull request workflows
After using agents heavily in internal workflows, the TRAE team summarized the 10 most frequently used Skills.
Agent Skills Top 10
1. frontend-design: Frontend Design
It is not surprising that frontend-design ranks first.
Many AI-generated interfaces may be functional, but the visual quality is often weak. Common problems include crowded layouts, random color choices, unclear component hierarchy, and a recognizable “AI default style.”
The value of this Skill is that it pushes AI to care more about real product experience, not just rendering features on a page.
For website owners, SaaS builders, and frontend developers, this is a highly practical direction.
2. cache-components: Component Caching
cache-components helps AI reuse components that have already been generated instead of creating new ones every time.
This reduces repeated work and can lower token usage.
In larger projects, component reuse also improves consistency. Buttons, forms, modals, cards, and layout containers should not be reinvented every time by the AI.
This type of Skill becomes especially important in long-running projects.
3. fullstack-developer: Full-Stack Development
fullstack-developer gives AI the working mode of a full-stack engineer.
It does not focus only on one isolated module. Instead, it pushes AI to consider frontend pages, backend APIs, database structure, data flow, and API design together.
This is especially valuable for indie developers.
Many indie hackers and small teams do not have a strict frontend-backend split. If AI can work with a full-stack perspective, it becomes much more useful than an assistant that only writes isolated code fragments.
4. frontend-code-review: Frontend Code Review
frontend-code-review focuses on reviewing frontend code.
Frontend issues are not limited to syntax errors. They can include poor component structure, messy state management, duplicated styles, weak accessibility, and incomplete responsive behavior.
The presence of this Skill shows that AI coding is moving into a more detailed quality-control stage.
The goal is not just to make the page run. The goal is to make the code reliable, readable, and maintainable.
5. code-reviewer: General Code Review
code-reviewer is a general-purpose code review Skill.
It can be used for broader quality checks, including logic errors, edge cases, error handling, performance issues, security risks, and maintainability problems.
Two review-related Skills appear in the top five. This suggests that ByteDance puts strong emphasis on AI code quality.
It also reflects a core issue in real AI coding adoption: output speed is not the only metric. Reliability matters more.
6. webapp-testing: Web App Testing
webapp-testing is used for testing web applications.
After AI writes code, the process should not stop at “it looks finished.” The app still needs to be checked: whether pages work, interactions behave correctly, and core user flows can be completed.
For real projects, testing Skills are an important condition for bringing AI agents into production workflows.
Without testing, AI-generated code carries much higher risk.
7. pr-creator: Automatic PR Creation
pr-creator is used to create pull requests automatically.
This Skill reflects workflow automation.
After code is completed, AI can summarize changes, generate a PR description, connect the work to task context, and move the change into the team’s normal collaboration process.
This matters for enterprise teams.
AI should not only be a local coding assistant. It also needs to fit into existing engineering workflows.
8. fix: Bug Fixing
fix maps to a very common development scenario: there is a problem here, please fix it.
This Skill looks simple, but it is used frequently in real work.
Developers often ask AI to locate and fix issues based on errors, logs, failed tests, or user feedback.
A good fix Skill should not only make the code run. It should also explain the root cause and avoid introducing new side effects.
9. update-docs: Documentation Updates
update-docs is used to keep documentation in sync after code changes.
This is one of the easiest things to ignore in real projects.
Code keeps changing, but README files, API docs, usage guides, and deployment instructions often fall behind. Over time, the docs lose trust.
Documentation updates are a very good task for agents.
They are not always complex, but they need to happen consistently.
10. find-skills: Skill Search and Discovery
find-skills is one of the most interesting items on the list.
It is essentially a meta-skill.
A normal Skill helps AI complete a task. find-skills helps AI decide what Skill it is missing and then search for or recommend the right one.
This means AI is not only passively using skills. It is starting to discover skills.
Over the long term, the best Agent Skill combinations may not be manually configured once by humans. They may be adjusted and evolved through continuous practice.
What This List Shows
This Top 10 list reveals several clear trends.
First, frontend-design ranking first shows that frontend taste has become a major pain point in AI coding.
Many teams do not just need an AI that can generate buttons and pages. They need an AI that can produce usable, good-looking interfaces that match the product.
Second, code quality is becoming more important than code speed.
The list includes frontend-code-review, code-reviewer, and webapp-testing. This shows that the TRAE team is not only focused on generating more code. It also places review and validation near the center of the workflow.
This matches the experience of many developers.
AI writes code quickly, but without review and testing, the long-term maintenance cost can become higher.
Third, agents are moving from “tool users” to “skill organizers.”
The appearance of find-skills is important.
It suggests that future AI coding systems may not rely only on fixed prompts or fixed rules. Instead, they may choose better working methods based on task type, project stage, and past failure patterns.
What Claude Code and Codex Users Can Learn
If you use Claude Code, Codex, or other AI coding tools, this list is a useful reference.
You can treat it as a blueprint for agent configuration, not just as a ranking inside one tool.
Several directions are useful for most AI coding workflows:
- Frontend design
- Component reuse
- Full-stack development
- Code review
- Web testing
- Documentation updates
- Bug fixing
These Skills cover the key steps from code generation to project maintenance.
For solo developers, frontend-design, code-reviewer, and update-docs are good starting points.
For teams, review, testing, PR creation, and documentation sync are more important to standardize first.
Recommendation
The value of this Agent Skills Top 10 list is not that one Skill ranks above another.
Its real value is that it points to a more mature way to use AI for programming.
AI coding should not only optimize for generating code faster.
The more useful direction is to let AI participate more reliably in the full engineering workflow, with clear loops around design, reuse, review, testing, delivery, and documentation.
If you are building your own AI coding workflow, this list is a practical starting point.
Start by making AI write more reliably. Then make it write faster.